The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Reinforcement learning in feedback control: Challenges and benchmarks from technical process control
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083
Cited by
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
High-Dimensional Continuous Control Using Generalized Advantage Estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1506.02438 · Cited by 2,103
Counterfactual Multi-Agent Policy Gradients
Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11794 · Cited by 1,873
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Levine, S., Kumar, A., Tucker, G., & Fu, J. (2020) · arXiv · doi:10.48550/arxiv.2005.01643 · Cited by 824
Metadata
- Authors
- Hafner, R., & Riedmiller, M.
- Venue
- Machine Learning
- Published
- 2011-02-27
- Volume
- 84
- Issue
- 1-2
- Pages
- 137-169
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1825869920
- Cited by
- 214
- References
- 2
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Reinforcement learning in feedback control: Challenges and benchmarks from technical process control. https://app.cochat.ai/papers/10.1007/s10994-011-5235-x
- The paper
- Hafner, R., & Riedmiller, M. (2011). Reinforcement learning in feedback control: Challenges and benchmarks from technical process control. Machine Learning. https://doi.org/10.1007/s10994-011-5235-x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.